Deploy NLLB-200 Translation API with Gradio

README.md

@@ -1,12 +1,74 @@
 ---
 title: NLLB200
-emoji: ⚡
-colorFrom: pink
-colorTo: indigo
+emoji: 🌍
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# NLLB-200 Translation API
+
+High-quality translation API powered by Meta's **No Language Left Behind (NLLB-200)** model.
+
+## Features
+
+- ✅ **200 Languages Supported** - Direct translation between any language pair
+- ✅ **44% Better Quality** - Compared to previous translation models
+- ✅ **+70% for Complex Languages** - Especially Arabic, Hindi, and other low-resource languages
+- ✅ **No Pivot Translation** - Direct translation without going through English
+- ✅ **Cached Results** - Faster repeated translations
+- ✅ **API Access** - Use via Gradio Client or HTTP
+
+## Supported Languages (Sample)
+
+Arabic, English, French, Spanish, German, Italian, Portuguese, Russian, Japanese, Korean, Chinese, Hindi, Turkish, Dutch, Polish, Swedish, Indonesian, Vietnamese, Thai, Ukrainian, Romanian, Greek, Hebrew, and more!
+
+## Usage
+
+### Web Interface
+
+Visit the Space and use the interactive interface to translate text between any supported languages.
+
+### API
+
+```python
+from gradio_client import Client
+
+client = Client("TGPro1/NLLB200")
+result = client.predict(
+    "Hello, world!",  # text
+    "English",         # source language
+    "Arabic",          # target language
+    api_name="/predict"
+)
+print(result)  # مرحبا بالعالم!
+```
+
+## Model
+
+- **Model**: `facebook/nllb-200-distilled-600M`
+- **Parameters**: 600M
+- **Size**: ~2.4GB
+- **Languages**: 200
+
+## Performance
+
+- **Quality**: State-of-the-art for low-resource languages
+- **Speed**: Fast inference with distilled model
+- **Cache**: LRU cache for frequently translated phrases
+
+## Credits
+
+- **Model**: Meta AI Research - [NLLB Project](https://ai.meta.com/research/no-language-left-behind/)
+- **Benchmark**: FLORES-200
+- **License**: Apache 2.0
+
+## Links
+
+- [Model Card](https://huggingface.co/facebook/nllb-200-distilled-600M)
+- [Research Paper](https://arxiv.org/abs/2207.04672)
+- [Full Language List](https://github.com/facebookresearch/flores/blob/main/flores200/README.md)

app.py

@@ -1,7 +1,237 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+from functools import lru_cache
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Language mappings for NLLB-200
+LANGUAGE_CODES = {
+    "Arabic": "arb_Arab",
+    "English": "eng_Latn",
+    "French": "fra_Latn",
+    "Spanish": "spa_Latn",
+    "German": "deu_Latn",
+    "Italian": "ita_Latn",
+    "Portuguese": "por_Latn",
+    "Russian": "rus_Cyrl",
+    "Japanese": "jpn_Jpan",
+    "Korean": "kor_Hang",
+    "Chinese (Simplified)": "zho_Hans",
+    "Hindi": "hin_Deva",
+    "Turkish": "tur_Latn",
+    "Dutch": "nld_Latn",
+    "Polish": "pol_Latn",
+    "Swedish": "swe_Latn",
+    "Arabic (Egyptian)": "arz_Arab",
+    "Arabic (Moroccan)": "ary_Arab",
+    "Indonesian": "ind_Latn",
+    "Vietnamese": "vie_Latn",
+    "Thai": "tha_Thai",
+    "Ukrainian": "ukr_Cyrl",
+    "Romanian": "ron_Latn",
+    "Greek": "ell_Grek",
+    "Hebrew": "heb_Hebr",
+}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Load model
+print("Loading NLLB-200 model...")
+model_name = "facebook/nllb-200-distilled-600M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Use GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+print(f"Model loaded on {device}")
+
+
+@lru_cache(maxsize=1000)
+def translate_cached(text, src_lang_code, tgt_lang_code):
+    """Cached translation to avoid repeating same translations"""
+    return translate(text, src_lang_code, tgt_lang_code)
+
+
+def translate(text, src_lang, tgt_lang):
+    """
+    Translate text using NLLB-200
+    
+    Args:
+        text: Text to translate
+        src_lang: Source language name
+        tgt_lang: Target language name
+    
+    Returns:
+        Translated text
+    """
+    if not text or not text.strip():
+        return ""
+    
+    # Get language codes
+    src_lang_code = LANGUAGE_CODES.get(src_lang, "eng_Latn")
+    tgt_lang_code = LANGUAGE_CODES.get(tgt_lang, "arb_Arab")
+    
+    # Use cached version
+    try:
+        return translate_cached(text.strip(), src_lang_code, tgt_lang_code)
+    except Exception as e:
+        return f"Translation error: {str(e)}"
+
+
+def translate_actual(text, src_lang_code, tgt_lang_code):
+    """Actual translation logic"""
+    tokenizer.src_lang = src_lang_code
+    inputs = tokenizer(text, return_tensors="pt", padding=True, max_length=512, truncation=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        translated_tokens = model.generate(
+            **inputs,
+            forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang_code],
+            max_length=512,
+            num_beams=5,
+            early_stopping=True
+        )
+    
+    translation = tokenizer.batch_decode(
+        translated_tokens, 
+        skip_special_tokens=True
+    )[0]
+    
+    return translation
+
+
+# Patch the cached function to use actual translation
+translate_cached.__wrapped__ = translate_actual
+
+
+def gradio_translate(text, src_lang, tgt_lang):
+    """Gradio interface function"""
+    if src_lang == tgt_lang:
+        return text
+    
+    result = translate(text, src_lang, tgt_lang)
+    return result
+
+
+# Available languages (sorted alphabetically)
+LANGUAGES = sorted(LANGUAGE_CODES.keys())
+
+
+# Create Gradio Interface
+with gr.Blocks(title="NLLB-200 Translation API", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🌍 NLLB-200 Translation API
+        
+        **Meta's No Language Left Behind** - 200 Languages Translation
+        
+        - ✅ High-quality translation for 200+ languages
+        - ✅ 44% better than previous models
+        - ✅ +70% improvement for complex languages (Arabic, Hindi, etc.)
+        - ✅ Direct translation (no pivot through English)
+        - ✅ Cached for faster repeated translations
+        
+        **Powered by**: `facebook/nllb-200-distilled-600M`
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            src_lang = gr.Dropdown(
+                choices=LANGUAGES,
+                value="English",
+                label="Source Language",
+                interactive=True
+            )
+            input_text = gr.Textbox(
+                label="Text to Translate",
+                placeholder="Enter text here...",
+                lines=5,
+                max_lines=10
+            )
+        
+        with gr.Column():
+            tgt_lang = gr.Dropdown(
+                choices=LANGUAGES,
+                value="Arabic",
+                label="Target Language",
+                interactive=True
+            )
+            output_text = gr.Textbox(
+                label="Translation",
+                lines=5,
+                max_lines=10,
+                interactive=False
+            )
+    
+    with gr.Row():
+        translate_btn = gr.Button("Translate 🚀", variant="primary", size="lg")
+        clear_btn = gr.Button("Clear", variant="secondary")
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["Hello, how are you?", "English", "Arabic"],
+            ["مرحبا، كيف حالك؟", "Arabic", "French"],
+            ["Bonjour, comment allez-vous?", "French", "English"],
+            ["This is a test of NLLB-200 translation model.", "English", "Spanish"],
+        ],
+        inputs=[input_text, src_lang, tgt_lang],
+        outputs=output_text,
+        fn=gradio_translate,
+        cache_examples=False
+    )
+    
+    # Event handlers
+    translate_btn.click(
+        fn=gradio_translate,
+        inputs=[input_text, src_lang, tgt_lang],
+        outputs=output_text
+    )
+    
+    clear_btn.click(
+        fn=lambda: ("", ""),
+        inputs=None,
+        outputs=[input_text, output_text]
+    )
+    
+    # Also translate on Enter key
+    input_text.submit(
+        fn=gradio_translate,
+        inputs=[input_text, src_lang, tgt_lang],
+        outputs=output_text
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### API Usage
+        
+        You can use this Space programmatically via the Gradio API:
+        
+        ```python
+        from gradio_client import Client
+        
+        client = Client("TGPro1/NLLB200")
+        result = client.predict(
+            "Hello, world!",  # text
+            "English",         # source language
+            "Arabic",          # target language
+            api_name="/predict"
+        )
+        print(result)
+        ```
+        
+        **Supported Languages**: 25+ major languages (see dropdown)
+        
+        For full list of 200 languages, check the [NLLB-200 documentation](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
+        """
+    )
+
+
+if __name__ == "__main__":
+    demo.queue(max_size=10)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )

requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.44.0
+transformers==4.36.2
+torch==2.1.2
+sentencepiece==0.1.99
+sacremoses==0.1.1
+accelerate==0.25.0